Adopting cloud computing presents significant opportunities for organizations, like offering scalability, cost efficiencies, and competitive advantages. However, many lack the necessary expertise to assess their cloud readiness independently. Current solutions often involve hiring costly external consultants or relying on biased and commercially driven assessments that cloud service providers offer. This lack of affordable, objective, and scientifically grounded tools creates a substantial barrier to cloud adoption.

This master thesis addresses this problem by proposing a new solution: a scientifically founded, holistic framework for cloud readiness assessment, implemented as a self-service tool on the abiliCor platform. The framework integrates the rigor of academic research with the accessibility and automation of commercial tools. This enables organizations to evaluate their readiness independently. The framework provides actionable insights through structured dimensions and weighted indicators, helping organizations identify improvement areas and make informed decisions for cloud adoption.

The research contributes to the field by bridging the gap between theoretical robustness and practical usability. It introduces an innovative approach to self-service assessments that combines expert knowledge, comprehensive evaluation criteria, and automated analysis. By empowering organizations with this tool, the thesis enhances their readiness for digital transformation and democratizes access to strategic cloud adoption resources. This work lays the groundwork for future developments in scalable and scientifically rigorous assessment methodologies.